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Details
We will start from beginning of data journey which was from early 2000 where RDBMS and ETL were ruling the world. We will move to Big data and then understands its complexities and then jump over the modern data platform implementations which includes Data Mesh and Data Fabric
Topics
Introduction to Data Landscape |
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Understand high level data data implementations through real case studies 1) RDBMS based DWH Implementation 2) Data Lake Implementation |
Traditional DWH & Data Modelling |
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We will learn the core concepts of DWH and Data modelling through case studies 1) DWH, Data Mart & EDW 2) Data Modelling 3) 3NF & Dimensional Data modelling |
ETL |
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Learn the ETL architecture and cover the following 1) Batch load ETL 2) File based ETL 3) Database extraction 4) Transformations 5) Loading 6) ETL, ELT, ETLT |
Master Data Management |
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Learn the Master data, and MDM concepts by going through use cases 1) Data Steward job 2) Merge and matching |
Data Governance, Data Quality & Metadata |
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Learn the governance side of data platforms by going though the following concepts 1) Data Governance principles 2) Data Quality rules 3) Metadata implementations |
Data Lake |
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Move over to next generation of data platform by understanding the big data world 1) Architecture 2) HDFS 3) Hive 4) Sqoop |
Real time processing |
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Learn the real time implementations architecture using Kafka and its components 1) Core Kafka 2) Kakfa connectors 3) KStream 4) KSQL |
Data Lake processing |
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Understands the jargons of data lake processing using Spark 1) Basic of Python 2) Spark using Python 3) Spark using Scala |
Modern Data Architecture |
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Understand data3.0 world and how it is redefining the world of data platforms 1) Data Mesh 2) Data Fabric 3) The Future |
Data on Cloud |
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Learn how cloud architecture is making differences to data world 1) Data on Azure 2) Data on AWS |